Geocomputation and Data Analysis with R

Course overview

The aim of Geocomputation and Data Analysis with R is to get you up-to-speed with high performance geographic processing, analysis, visualisation and modelling capabilities from the command-line. The course will be delivered in R, a statistical programming language popular in academia, industry and, increasingly, the public sector. It will teach a range of techniques using recent developments in the package sf and the ‘metapackage’ tidyverse, based on the open source book Geocomputation with R (Lovelace, Nowosad, and Meunchow 2019).

Learning Objectives

By the end of the course participants should:

  • Be able to use R and RStudio as a powerful Geographic Information System (GIS)
  • Know how R’s spatial capabilities fit within the landscape of open source GIS software
  • Be confident with using R’s command-line interface (CLI) and scripting capabilities for geographic data processing
  • Understand how to import a range of data sources into R
  • Be able to perform a range of attribute operations such as subsetting and joining
  • Understand how to implement a range of spatial data operations including spatial subsetting and spatial aggregation
  • Have the confidence to output the results of geographic research in the form of static and interactive maps

Prior reading/ experience

If you are new to R, ensure you have completed a basic introductory course such as DataCamp’s introduction to R free course or equivalent.

If you’re interested in R for ‘data science’ and installing/updating/choosing R packages, these additional resources are recommended (these optional resources are all freely available online):

Who should attend?

The course is open to students, academic staff and external delegates.


Robin Lovelace is a researcher at the Leeds Institute for Transport Studies (ITS) and the Leeds Institute for Data Analytics (LIDA). Robin has many years of experience of using R for academic research and has taught numerous R courses at all levels. He has developed popular R resources including the recently published book Efficient R Programming (Gillespie and Lovelace 2016), Introduction to Visualising Spatial Data in R and Spatial Microsimulation with R (Lovelace and Dumont 2016). These skills have been applied on a number of projects with real-world applications, including the Propensity to Cycle Tool, a nationally scalable interactive online mapping application, and the stplanr package.

Fee information

This course has now finished. Should you have any enquiries, please email

Venue details

Leeds Institute for Data Analytics

Level 11, Worsley Building

Clarendon Way



Contact us

Institute for Transport Studies
Leeds LS2 9JT